**R Programming in Data Science: Dates and Times**

English | MP4 | AVC 1280×720 | AAC 48KHz 2ch | 2h 17m | 325 MB

One of the fundamental difficulties of data science is working with dates and times. This course shows data engineers, DevOps practitioners, and data-science programmers the most common (and many not so common!) problems and how to use R-based tools to implement solutions. Learn how dates and times are stored and retrieved in base R. Find out how to format, compare, add and subtract, and extract dates and times using built-in R functions. Then discover how to incorporate specialized R packages, such as lubridate, busdater, zoo, timelineR, anytime, datetime, and more, to perform some of the heavy lifting. Instructor Mark Niemann-Ross walks you through each package, so you can appreciate the advantages and best uses of each one.

Topics include:

- Choosing the right tool
- Dates and times in base R
- Dealing with time zones
- Adding and subtracting dates and times
- Formatting dates and times
- Rounding dates and times
- Using lubridate for dates and times
- Business and finance packages for R
- Working with time-series data
- Specialized data and time packages

**Table of Contents**

1 Calculating times and dates with R

2 Course organization

3 Typical date calculations

4 How dates and times are stored in R

5 Choose the right date and time tool

6 The base R Date class

7 Use formatters to recognize dates in character strings

8 Dealing with time zones and daylight savings time

9 Use operators to compare date objects

10 Adding and subtracting dates and times

11 Create sequences of dates, cut dates, and round dates

12 Extract parts of a date

13 Presenting formatted dates and times

14 Use read.csv() to import CSV date information

15 Advantages of the Lubridate package

16 Parsing date and time with Lubridate

17 Getting and setting time components with Lubridate

18 Rounding dates and time with Lubridate

19 Lubridate math with durations

20 Lubridate math with periods

21 Lubridate math with intervals

22 Time zones with Lubridate

23 The busdater package

24 The BusinessDuration package

25 The fmdates package

26 Time-series data

27 The base R ts class

28 The zoo package

29 The xts package

30 The tsibble and tibbletime packages

31 Time-series rolling statistics

32 Time-series graphics

33 The timelineR package

34 The timelineS package

35 The CRAN task view for time-series analysis

36 The anytime package

37 The hms package

38 The mondate package

39 The datetime package

40 The datetimeutils package

41 The padr package

42 Next steps

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